Visualizing Similarity Data with a Mixture of Maps

James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey Hinton
; Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:67-74, 2007.

Abstract

We show how to visualize a set of pairwise similarities between objects by using several different two-dimensional maps, each of which captures different aspects of the similarity structure. When the objects are ambiguous words, for example, different senses of a word occur in different maps, so “river” and “loan” can both be close to “bank” without being at all close to each other. Aspect maps resemble clustering because they model pair-wise similarities as a mixture of different types of similarity, but they also resemble local multi-dimensional scaling because they model each type of similarity by a twodimensional map. We demonstrate our method on a toy example, a database of human wordassociation data, a large set of images of handwritten digits, and a set of feature vectors that represent words.

Cite this Paper


BibTeX
@InProceedings{pmlr-v2-cook07a, title = {Visualizing Similarity Data with a Mixture of Maps}, author = {James Cook and Ilya Sutskever and Andriy Mnih and Geoffrey Hinton}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {67--74}, year = {2007}, editor = {Marina Meila and Xiaotong Shen}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/cook07a/cook07a.pdf}, url = {http://proceedings.mlr.press/v2/cook07a.html}, abstract = {We show how to visualize a set of pairwise similarities between objects by using several different two-dimensional maps, each of which captures different aspects of the similarity structure. When the objects are ambiguous words, for example, different senses of a word occur in different maps, so “river” and “loan” can both be close to “bank” without being at all close to each other. Aspect maps resemble clustering because they model pair-wise similarities as a mixture of different types of similarity, but they also resemble local multi-dimensional scaling because they model each type of similarity by a twodimensional map. We demonstrate our method on a toy example, a database of human wordassociation data, a large set of images of handwritten digits, and a set of feature vectors that represent words.} }
Endnote
%0 Conference Paper %T Visualizing Similarity Data with a Mixture of Maps %A James Cook %A Ilya Sutskever %A Andriy Mnih %A Geoffrey Hinton %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-cook07a %I PMLR %J Proceedings of Machine Learning Research %P 67--74 %U http://proceedings.mlr.press %V 2 %W PMLR %X We show how to visualize a set of pairwise similarities between objects by using several different two-dimensional maps, each of which captures different aspects of the similarity structure. When the objects are ambiguous words, for example, different senses of a word occur in different maps, so “river” and “loan” can both be close to “bank” without being at all close to each other. Aspect maps resemble clustering because they model pair-wise similarities as a mixture of different types of similarity, but they also resemble local multi-dimensional scaling because they model each type of similarity by a twodimensional map. We demonstrate our method on a toy example, a database of human wordassociation data, a large set of images of handwritten digits, and a set of feature vectors that represent words.
RIS
TY - CPAPER TI - Visualizing Similarity Data with a Mixture of Maps AU - James Cook AU - Ilya Sutskever AU - Andriy Mnih AU - Geoffrey Hinton BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics PY - 2007/03/11 DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-cook07a PB - PMLR SP - 67 DP - PMLR EP - 74 L1 - http://proceedings.mlr.press/v2/cook07a/cook07a.pdf UR - http://proceedings.mlr.press/v2/cook07a.html AB - We show how to visualize a set of pairwise similarities between objects by using several different two-dimensional maps, each of which captures different aspects of the similarity structure. When the objects are ambiguous words, for example, different senses of a word occur in different maps, so “river” and “loan” can both be close to “bank” without being at all close to each other. Aspect maps resemble clustering because they model pair-wise similarities as a mixture of different types of similarity, but they also resemble local multi-dimensional scaling because they model each type of similarity by a twodimensional map. We demonstrate our method on a toy example, a database of human wordassociation data, a large set of images of handwritten digits, and a set of feature vectors that represent words. ER -
APA
Cook, J., Sutskever, I., Mnih, A. & Hinton, G.. (2007). Visualizing Similarity Data with a Mixture of Maps. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in PMLR 2:67-74

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